Antibodies play a critical role in the immune system for recognition of foreign intruders. Because of their excellent affinity and specificity, they hav also been exploited as therapeutic molecules and biotechnological components for sensing and assembly. Structures of antibodies in complex with their antigens can yield insight into biological phenomena or drug and disease mechanisms. However, structures of antibodies and antibody-antigen complexes can be difficult, time consuming, and expensive to determine. The proposed research focuses on the computational prediction of the structure of antibodies and antibody-antigen complexes. Computational approaches are particularly important because the repertoire of antibodies in a human patient is far too large for complete structural characterization by experiment. Prior work has isolated the most critical challenges: most of the antibodies in the human repertoire have hypervariable CDR H3 loops longer than that which is predictable using current loop methods; backbone conformational uncertainty and flexibility confound current docking methods; and no current method can quantitatively predict antibody-antigen binding affinities from structure. Thus, the first three aims of the project are to (1) develop new methods to predict the structure of long CDR H3 loops using statistics to identify likely turns, (2) develop flexible backbone docking routines using an expanded ensemble approach with a conformational web, and (3) develop methods to quantitatively predict protein-protein binding affinity using improved electrostatics treatments. Finally, the fourth aim will be to (4) use existng and proposed methods to predict structures of antibodies and antibody-antigen complexes for entire polyclonal antibody repertoires. Structures will be predicted for antibody repertoires determined from bone marrow plasma cells of mice immunized against ovalbumin (a food allergen) and enzyme C1s (a therapeutic target for autoimmune diseases and transplant tolerance). Ultimately, these studies will yield insights into immunology, molecular recognition, and design of protein-protein interfaces and vaccines.